A trading risk assessment method, device, electronic device, and storage medium

Abstract

The present application discloses a trading risk assessment method, apparatus, electronic device, and storage medium. The method includes: statistically calculating the trading price deviation degree and trading volume within a target time range according to a first time dimension; verifying whether each of the trading price deviation degrees and the trading volume conform to a normal distribution; if it is detected that each of the trading price deviation degrees and the trading volume does not conform to a normal distribution, then determining whether each of the trading price deviation degrees belonging to the fat tail part is greater than a threshold; if it is determined that each of the trading price deviation degrees belonging to the fat tail part is not greater than the threshold, then finding out each preliminary selected our trader and preliminary selected counterparty trading institution whose negative deviation amount meets the preliminary selection conditions; determining a high-risk object based on the trading data within the time range; counting the number of occurrences of the determined high-risk object, and performing risk assessment based on the number of occurrences.

Description

Technical Field

[0001] This application relates to the technical field of risk assessment, and particularly relates to a transaction risk assessment method and device, an electronic device, and a storage medium. Background Art

[0002] In the bond market, interest transfer has always been a key concern of regulatory agencies, especially the implicit interest transfer mainly achieved through abnormal transaction prices.

[0003] Therefore, in order to ensure the risk of transactions, the currently mainly adopted method is price review, that is, setting a deviation threshold range for the transaction price, and then manually reviewing each transaction falling within this range to determine the trader on our side, the counterparty transaction, and the counterparty institution that may have interest transfer.

[0004] However, in the existing methods, it is very difficult to determine the deviation interval threshold scientifically and reasonably and adjust it dynamically in a timely manner, so the accuracy of the review results cannot be guaranteed. Moreover, for transactions that do not fall within the interval, single small deviation transactions are ignored. Summary of the Invention

[0005] Based on the above deficiencies of the prior art, this application provides a transaction risk assessment method and device, an electronic device, and a storage medium to solve the problem that the prior art cannot guarantee the accuracy of the results.

[0006] To achieve the above object, this application provides the following technical solutions:

[0007] An embodiment of this application provides a transaction risk assessment method, including:

[0008] Statistically calculate the transaction price deviation degree and trading volume within the target time range according to the first time dimension;

[0009] Verify whether each of the transaction price deviation degrees and the trading volume conform to the normal distribution;

[0010] If it is detected that each of the transaction price deviation degrees and the trading volume does not conform to the normal distribution, then determine whether each of the transaction price deviation degrees belonging to the fat tail part is greater than the threshold;

[0011] If it is determined that each of the transaction price deviation degrees belonging to the fat tail part is not greater than the threshold, then find out each primary trader on our side and the primary counterparty trading institution whose negative deviation amount meets the primary selection conditions;

[0012] Based on the transaction data within the said time range, high-risk objects are determined; among them, the high-risk objects include the first trader on our side, the first counterparty trading institution, the first counterparty trader, the second counterparty trader, the third counterparty trader, the fourth counterparty trader, the second trader on our side, the second counterparty trading institution, the third trader on our side, and the fifth counterparty trader; the first trader on our side and the first counterparty trading institution refer to the trader on our side and the counterparty trading institution whose deviation indicators meet the corresponding conditions; the first counterparty trader, the second counterparty trader, and the third counterparty trader refer to the counterparty traders whose deviation data and trading volume indicators meet the corresponding conditions; the fourth counterparty trader refers to the counterparty trader who continuously conducts negatively deviated transactions; the second trader on our side and the second counterparty trading institution refer to the trader on our side and the counterparty trading institution that are suspected of reselling to each other; the third trader on our side and the fifth counterparty trader refer to the trader on our side and the counterparty trader whose trading quotes are abnormal or have a large number of negatively deviated transactions.

[0013] Count the occurrence times of the determined high-risk objects, and conduct risk assessment based on the occurrence times.

[0014] Optionally, in the above trading risk assessment method, the high-risk object is the first trader on our side, and the determining of the high-risk object based on the transaction data within the said time range includes:

[0015] Based on the transaction data within the said time range, count the total negative deviation amount of each trader on our side within the target time range according to the second time dimension, as well as the proportion of the total negative deviation amount to the total personal trading amount;

[0016] From each trader on our side, screen out the traders on our side whose total negative deviation amount ranks among the top N and the proportion of the total negative deviation amount to the total personal trading amount ranks among the top M, and determine them as the first trader on our side.

[0017] Optionally, in the above trading risk assessment method, the high-risk object is the first counterparty trading institution, and the determining of the high-risk object based on the transaction data within the said time range includes:

[0018] Based on the transaction data within the said time range, count the positive deviation amount and negative deviation amount of each counterparty trading institution within the target time range according to the second time dimension;

[0019] Add the positive deviation amount and negative deviation amount of each counterparty trading institution respectively to obtain the deviation summary value of each counterparty trading institution;

[0020] Among the opponent trading institutions with the absolute values of the deviation summary values ranked in the top L, the opponent trading institution with the smallest deviation summary value is determined as the first opponent trading institution.

[0021] Optionally, in the above trading risk assessment method, the high-risk objects are the first opponent trader, the second opponent trader, and the third opponent trader. Determining the high-risk objects based on the trading data within the time range includes:

[0022] Based on the trading data within the time range, count, according to the second time dimension, the total trading volume, the number of negative deviation transactions, the deviation summary value, and the ratio of the negative amount to the personal trading volume between each of our traders and each opponent trader within the target time range;

[0023] For each of our traders, the opponent traders with the deviation summary value and the ratio of the negative amount to the personal trading volume both ranked in the top J are determined as the first opponent trader;

[0024] Among the opponent traders with the trading volume ranked in the top K with respect to our trader, the opponent trader with the largest ratio of the negative amount to the total personal trading volume is determined as the second opponent trader;

[0025] Screen out the opponent traders with negative deviations in all transactions with our trader, and among the screened opponent traders, the opponent trader with the largest deviation summary value among the opponent traders with the number of negative deviation transactions ranked in the top Q is determined as the third opponent trader.

[0026] Optionally, in the above trading risk assessment method, the high-risk object is the fourth opponent trader. Determining the high-risk object based on the trading data within the time range includes:

[0027] Based on the trading data within the time range, count, according to the second time dimension, the deviation amount of each transaction between each of our traders and each opponent trader within the target time range;

[0028] The opponent trader with the deviation amount being negative for more than W consecutive transactions with any one of our traders is determined as the fourth opponent trader.

[0029] Optionally, in the above trading risk assessment method, the high-risk objects are the second our trader and the second opponent trading institution. Determining the high-risk objects based on the trading data within the time range includes:

[0030] Based on the transaction data within the time range, count the trading volume of the first target transaction within the target time range according to the second time dimension; wherein, the first target transaction refers to the transaction of the same tradable product with the same trading volume at the same price on the same day.

[0031] Based on the trading volume of the first target transaction, the own institution conducting the first target transaction, and the counterparty trading institution, draw a first transaction graph; wherein, the larger the trading volume of the first target transaction, the thicker the connection line between the corresponding nodes of the own institution and the counterparty trading institution of the first target transaction.

[0032] Based on the first transaction graph, screen out each of the our traders and counterparty trading institutions that meet the first preset rule as the second our traders and the second counterparty trading institutions.

[0033] Optionally, in the above trading risk assessment method, the high-risk objects are the third our traders and the fifth counterparty traders. Determining the high-risk objects based on the transaction data within the time range includes:

[0034] Based on the transaction data within the time range, count the second target transactions of the same tradable product and the same counterparty trader, and where the negative deviation amount is greater than the positive deviation amount according to the second time dimension.

[0035] Based on the negative deviation amount of the second target transaction, the our trader and the counterparty trader conducting the second target transaction, draw a second transaction graph.

[0036] Based on the second transaction graph, determine the our traders with the number of negative deviation transactions with the same counterparty trader greater than the preset quantity requirement, and the our traders whose quotation levels meet the preset low quotation standard as the third our traders.

[0037] Determine the counterparty traders with the number of negative deviation transactions with the same our trader greater than the preset quantity requirement, and the counterparty traders whose quotation levels meet the preset high quotation standard as the fifth counterparty traders.

[0038] The second aspect of the present application provides a trading risk assessment device, including:

[0039] A statistics unit for counting the trading price deviation degree and trading volume within the target time range according to the first time dimension.

[0040] A verification unit for verifying whether each of the trading price deviation degrees and the trading volumes conform to the normal distribution.

[0041] A judgment unit, configured to judge whether each of the trading price deviations belonging to the fat tail part is greater than a threshold when it is detected that each of the trading price deviations and the trading volume do not conform to the normal distribution;

[0042] A primary selection unit, configured to find out each primary self-trader, primary counterparty trader, and primary counterparty institution whose negative deviation amount meets the primary selection conditions when it is judged that each of the trading price deviations belonging to the fat tail part is not greater than the threshold;

[0043] An object determination unit, configured to determine high-risk objects based on the trading data within the time range; wherein, the high-risk objects include a first self-trader, a first counterparty institution, a first counterparty trader, a second counterparty trader, a third counterparty trader, a fourth counterparty trader, a second self-trader, a second counterparty institution, a third self-trader, and a fifth counterparty trader; the first self-trader and the first counterparty institution refer to the self-trader and the counterparty institution whose deviation indicators meet the corresponding conditions; the first counterparty trader, the second counterparty trader, and the third counterparty trader refer to the counterparty traders whose deviation data and trading volume indicators meet the corresponding conditions; the fourth counterparty trader refers to the counterparty trader who continuously conducts negatively deviated transactions; the second self-trader and the second counterparty institution refer to the self-trader and the counterparty institution that are suspected of reselling to each other; the third self-trader and the fifth counterparty trader refer to the self-trader and the counterparty trader whose trading quotes are abnormal or have a large number of negatively deviated transactions;

[0044] A risk assessment unit, configured to count the occurrence times of the determined high-risk objects and perform risk assessment based on the occurrence times.

[0045] A third aspect of the present application provides an electronic device, including:

[0046] A memory and a processor;

[0047] Wherein, the memory is used to store a program;

[0048] The processor is used to execute the program, and when the program is executed, it is specifically used to implement the trading risk assessment method described in any one of the above.

[0049] A fourth aspect of the present application provides a computer storage medium, used to store a computer program, and when the computer program is executed, it is used to implement the trading risk assessment method described in any one of the above.

[0050] The present application provides a trading risk assessment method, which statistically calculates the trading price deviation degree and trading volume within a target time range according to the first time dimension. Then, it verifies whether each trading price deviation degree and trading volume conform to the normal distribution. If it is detected that each trading price deviation degree and trading volume do not conform to the normal distribution, it is determined whether each trading price deviation degree belonging to the fat tail part is greater than the threshold. If it is determined that each trading price deviation degree belonging to the fat tail part is not greater than the threshold, each preliminary-selected our trader and preliminary-selected counterparty trading institution whose negative deviation amount meets the preliminary selection conditions are found. After the preliminary selection, based on the trading data within the time range, high-risk objects are determined. Among them, the high-risk objects include the first our trader, the first counterparty trading institution, the first counterparty trader, the second counterparty trader, the third counterparty trader, the fourth counterparty trader, the second our trader, the second counterparty trading institution, the third our trader, and the fifth counterparty trader; the first our trader and the first counterparty trading institution refer to our trader and counterparty trading institution whose deviation indicators meet the corresponding conditions; the first counterparty trader, the second counterparty trader, and the third counterparty trader refer to counterparty traders whose deviation data and trading volume indicators meet the corresponding conditions; the fourth counterparty trader refers to the counterparty trader who continuously conducts negatively deviated transactions; the second our trader and the second counterparty trading institution refer to our trader and counterparty trading institution with suspicion of reselling between each other; the third our trader and the fifth counterparty trader refer to our trader and counterparty trader with abnormal trading quotes or a large number of negatively deviated transactions. The occurrence times of the determined high-risk objects are statistically counted, and risk assessment is carried out based on the occurrence times. Thus, risk assessment is realized from relevant dimensions of price review, making the result more accurate, and analyzing the overall transaction to avoid ignoring single small-deviation transactions. Description of the Drawings

[0051] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are only the embodiments of the present application. For those of ordinary skill in the art, other drawings can be obtained according to the provided drawings without creative efforts.

[0052] Figure 1 It is a flowchart of a trading risk assessment method provided by an embodiment of the present application;

[0053] Figure 2 It is a flowchart of a method for determining the first our trader provided by an embodiment of the present application;

[0054] Figure 3 It is a flowchart of a method for determining the first counterparty trading institution provided by an embodiment of the present application;

[0055] Figure 4 Flow chart of a method for determining a first counterparty trader, a second counterparty trader, and a third counterparty trader provided by an embodiment of the present application;

[0056] Figure 5 Flow chart of a method for determining a fourth counterparty trader provided by an embodiment of the present application;

[0057] Figure 6 Flow chart of a method for determining a second trader on our side and a second counterparty institution provided by an embodiment of the present application;

[0058] Figure 7 Schematic diagram of a first trading map provided by an embodiment of the present application;

[0059] Figure 8 Flow chart of a method for determining a third trader on our side and a fifth counterparty trader provided by an embodiment of the present application;

[0060] Figure 9 Schematic diagram of a second trading map provided by an embodiment of the present application;

[0061] Figure 10 Schematic diagram of the structure of a trading risk assessment device provided by an embodiment of the present application;

[0062] Figure 11 Schematic diagram of the structure of an electronic device provided by an embodiment of the present application. Detailed implementation manners

[0063] Next, the technical solutions in the embodiments of the present application will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present application.

[0064] In the present application, relational terms such as "first" and "second" are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the term "comprising", "including" or any other variant thereof is intended to cover a non-exclusive inclusion, so that a process, method, article or device including a series of elements not only includes those elements, but also includes other elements not expressly listed, or further includes elements inherent to such process, method, article or device. Without more limitations, the element defined by the statement "including a..." does not exclude the existence of additional identical elements in the process, method, article or device including the element.

[0065] An embodiment of the present application provides a trading risk assessment method, as Figure 1 shown, including:

[0066] S101. Statistically analyze the trading price deviation and trading volume within the target time range according to the first time dimension.

[0067] Optionally, the first time dimension may be a quarter, that is, statistically analyze the trading price deviation and trading volume within the target time range on a quarterly basis.

[0068] S102. Verify whether each trading price deviation and trading volume conform to the normal distribution.

[0069] Optionally, the normal curve histogram or the K-S verification method can be used to verify whether each trading price deviation and trading volume conform to the normal distribution. If it is verified that each trading price deviation and trading volume do not conform to the normal distribution, it indicates that the price fairness of the transactions in this quarter is insufficient. Therefore, it is necessary to further analyze whether the deviation of the data in the "fat tail" part is reasonable. So, step S103 is executed at this time. If it is verified that each trading price deviation and trading volume conform to the normal distribution, step S105 can be directly executed.

[0070] S103. Determine whether each trading price deviation belonging to the fat tail part is greater than the threshold.

[0071] It should be noted that for the data in the fat tail part, first pay attention to whether its deviation degree is too large. Therefore, it is necessary to first determine whether it is greater than the threshold. If it is greater than the threshold, then focus on recording and analyzing the number of breakthrough transactions, deviation dates, and amounts, and relevant personnel need to trace back the market situation to find the reasons for the deviation.

[0072] If it is determined that each trading price deviation belonging to the fat tail part is not greater than the threshold, then it is necessary to focus on the statistical analysis of "negative deviation". So, step S104 is executed at this time. If it is not greater than the threshold, then step S105 can be directly executed.

[0073] S104. Find out each initial candidate of our trader and the initial candidate of the counterparty trading institution whose negative deviation amount meets the initial selection conditions.

[0074] Specifically, subtract the trading volume from the deviation price difference, and the negative deviation amount can be obtained. Based on the negative deviation amount, from the dimensions of our traders and counterparty trading institutions, statistically analyze the traders and counterparty trading institutions with a large negative deviation amount, that is, determine the initial candidate of our trader and the initial candidate of the counterparty trading institution that meet the initial selection conditions, and focus on them.

[0075] S105. Determine the high-risk objects based on the trading data within the time range.

[0076] It should be noted that after preliminary screening, in the embodiments of the present application, more comprehensive data mining will be carried out, that is, based on the transaction data within a time range, high-risk objects will be more comprehensively determined.

[0077] Among them, the high-risk objects include the first trader on our side, the first counterparty trading institution, the first counterparty trader, the second counterparty trader, the third counterparty trader, the fourth counterparty trader, the second trader on our side, the second counterparty trading institution, the third trader on our side, and the fifth counterparty trader.

[0078] The first trader on our side and the first counterparty trading institution refer to the trader on our side and the counterparty trading institution whose deviation indicators meet the corresponding conditions. Among them, the deviation indicator is an indicator related to price deviation and is not limited to a specific indicator. And the deviation indicators used to determine the first trader on our side and the first counterparty trading institution can be different.

[0079] The first counterparty trader, the second counterparty trader, and the third counterparty trader refer to the counterparty traders whose deviation data and trading volume indicators meet the corresponding conditions.

[0080] The fourth counterparty trader refers to the counterparty trader who continuously conducts negatively deviated transactions.

[0081] The second trader on our side and the second counterparty trading institution refer to the trader on our side and the counterparty trading institution that are suspected of reselling to each other.

[0082] The third trader on our side and the fifth counterparty trader refer to the trader on our side and the counterparty trader whose trading quotes are abnormal or have a large number of negatively deviated transactions.

[0083] Specifically, when the high-risk object is the first trader on our side, the specific implementation manner of step S105, that is, the method for determining the first trader on our side based on the transaction data within a time range, is as Figure 2 shown, including:

[0084] S201. Based on the transaction data within a time range, statistically calculate the total negative deviation and the proportion of the total negative deviation to the total personal transaction volume of each trader on our side within the target time range according to the second time dimension.

[0085] Optionally, the second time dimension can be year, that is, the data is statistically calculated annually.

[0086] S202. Select the traders on our side whose total negative deviation ranks among the top N and the proportion of the total negative deviation to the total personal transaction volume ranks among the top M from all the traders on our side, and determine them as the first traders on our side.

[0087] Optionally, N can be 3, and M can also be 3. Of course, other values can also be set according to the settings.

[0088] It should be noted that in the embodiments of the present application, the sorting of each value is in descending order of absolute value.

[0089] Specifically, it can be to screen out the top 3 of our traders with the highest total negative deviation, and screen out the top 3 of our traders with the largest ratio of the total negative deviation to the total personal transactions. Since the traders screened out twice indicate a large number of negatively deviated transactions and are relatively suspicious, they are determined as the first group of our traders.

[0090] Specifically, when the high-risk object is the first counterparty trading institution, the specific implementation manner of step S105, that is, the method for determining the first counterparty trading institution based on the trading data within the time range, is as Figure 3 shown, including:

[0091] S301. Based on the trading data within the time range, statistically calculate the positive deviation amount and negative deviation amount of each counterparty trading institution within the target time range according to the second time dimension.

[0092] S302. Add the positive deviation amount and negative deviation amount of each counterparty trading institution respectively to obtain the deviation summary value of each counterparty trading institution.

[0093] S303. Among the counterparty trading institutions whose absolute values of the deviation summary values rank among the top L, the counterparty trading institution with the smallest deviation summary value is determined as the first counterparty trading institution.

[0094] Optionally, L can be set to 10 or other positive integers.

[0095] Specifically, it can first determine the counterparty trading institutions whose absolute values of the deviation summary values rank among the top L, and then select the counterparty trading institution with the largest negative value from these L counterparty trading institutions. Since it is screened, it indicates that the transactions with this counterparty trading institution are relatively concentrated and there are many negatively deviated transactions, so it is relatively suspicious. Therefore, it is determined as the first counterparty trading institution.

[0096] Specifically, when the high-risk objects are the first counterparty trader, the second counterparty trader, and the third counterparty trader, the specific implementation manner of step S105, that is, the method for determining the first counterparty trader, the second counterparty trader, and the third counterparty trader based on the trading data within the time range, is as Figure 4 shown, including:

[0097] S401. Based on the transaction data within a time range, statistically calculate the total transaction volume, the number of negative deviation transactions, the deviation summary value, and the ratio of the negative amount to the individual transaction volume between each of our traders and each of the counterparty traders within the target time range according to the second time dimension.

[0098] S402. For each of our traders, determine the counterparty traders ranked among the top J in terms of both the deviation summary value and the ratio of the negative amount to the individual transaction volume as the first counterparty traders.

[0099] Since the statistics are mainly carried out for each of our traders to identify the counterparty traders with whom a particular one of our traders has overly frequent transactions or whose results are already known, it is necessary to use the data statistically calculated for each of our traders for screening.

[0100] Optionally, J can be 2 or other values.

[0101] Specifically, it can be to first mark the top two counterparty traders with the largest deviation summary value, and then mark the top two counterparty traders with the largest ratio of the negative amount to the individual transaction volume. If a counterparty trader is not marked twice, it indicates that its transactions with this one of our traders are relatively normal and there are more negative deviation transactions, so the suspicion is relatively high. Therefore, it is determined as the first counterparty trader.

[0102] S403. Among the counterparty traders ranked among the top K in terms of the transaction volume with this one of our traders, determine the counterparty trader with the largest ratio of the negative amount to the total individual transaction volume as the second counterparty trader.

[0103] Optionally, K can be set to 5 or other values.

[0104] Specifically, it can be to first list the counterparty traders ranked among the top K in terms of the transaction volume with this one of our traders, then mark the counterparty traders with a negative deviation summary value from them, and finally select the counterparty trader with the largest ratio of the negative amount to the total individual transaction volume as the second counterparty trader.

[0105] S404. Screen out the counterparty traders with negative deviations in all transactions with this one of our traders, and among the screened counterparty traders, determine the counterparty trader with the largest deviation summary value among the counterparty traders ranked among the top Q in terms of the number of negative deviation transactions as the third counterparty trader.

[0106] Specifically, when the high-risk object is the fourth counterparty trader, the specific implementation manner of step S105, that is, the method for determining the fourth counterparty trader based on the transaction data within a time range, is as Figure 5 shown, including:

[0107] S501. Based on the transaction data within a time range, statistically calculate the deviation amount of each transaction between each of our traders and various counterparty traders within the target time range according to the second time dimension.

[0108] S502. Identify the counterparty traders with a negative deviation amount for more than W consecutive transactions with any one of our traders as the fourth counterparty traders.

[0109] Optionally, statistically calculate each transaction between each of our traders and each counterparty trader according to the annual trading time sequence, and visually record the consecutive negative deviations for subsequent analysis. Since the suspicion of consecutive negative deviation transactions is relatively high, the counterparty traders with longer consecutive negative deviation transactions are identified as the fourth counterparty traders.

[0110] Specifically, when the high-risk objects are the second of our traders and the second counterparty trading institution, the specific implementation manner of step S105, that is, the method for determining the second of our traders and the second counterparty trading institution based on the transaction data within a time range, is as Figure 6 shown, including:

[0111] S601. Based on the transaction data within a time range, statistically calculate the trading volume of the first target transaction within the target time range according to the second time dimension.

[0112] Among them, the first target transaction refers to multiple transactions of the same tradable product with the same trading volume at the same price on the same day.

[0113] S602. Draw the first transaction graph based on the trading volume of the first target transaction, the self-institution conducting the first target transaction, and the counterparty trading institution.

[0114] Among them, the larger the trading volume of the first target transaction, the thicker the connection line between the nodes corresponding to the self-institution and the counterparty trading institution of the first target transaction.

[0115] Specifically, as Figure 7 shown, take the self-institution and the counterparty trading institution conducting the first target transaction as nodes respectively. Among them, the square represents our trader, and the triangle represents the counterparty trading institution. And connect the two parties that have conducted the first target transaction with a line, and represent it by the thickness of the connection line.

[0116] S603. Screen out each of our traders and counterparty trading institutions that meet the first preset rule from the first transaction graph as the second of our traders and the second counterparty trading institution.

[0117] For example, as Figure 7The trader on our side and the counterparty trading institution in the shown circle are connected by a relatively thick line. Therefore, there are a large number of first target transactions, so there is a suspicion of reselling transactions. Therefore, they are determined as the second trader on our side and the second counterparty trading institution. Specifically, the first preset rule can be preset to screen out the second trader on our side and the second counterparty trading institution from the first graph.

[0118] Specifically, when the high-risk objects are the third trader on our side and the fifth counterparty trader, the specific implementation manner of step S105, that is, the method for determining the third trader on our side and the fifth counterparty trader based on the trading data within the time range, is as Figure 8 shown, including:

[0119] S801. Based on the trading data within the time range, count the second target transactions of the same trading product and the same counterparty trader according to the second time dimension, and the negative deviation amount is greater than the positive deviation amount.

[0120] S802. Based on the negative deviation amount of the second target transaction, the trader on our side and the counterparty trader who conduct the second target transaction, draw the second trading graph.

[0121] Specifically, take the two parties who conduct the second target transaction as nodes. For example, as Figure 9 shown, the square represents the trader on our side, the triangle represents the counterparty trader, and the trading parties are connected by lines. Moreover, for the trader on our side with a larger negative deviation amount of the transaction, the size of the corresponding node is larger.

[0122] S803. Based on the second trading graph, determine the trader on our side whose negative deviation transaction quantity with the same counterparty trader is greater than the preset quantity requirement, and the trader on our side whose quotation level meets the preset low quotation standard as the third trader on our side.

[0123] S804. Determine the counterparty trader whose negative deviation transaction quantity with the same trader on our side is greater than the preset quantity requirement, and the counterparty trader whose quotation level meets the preset high quotation standard as the fifth counterparty trader.

[0124] For example, as Figure 9, the left circle shows that there is a large negative deviation of our trader from a certain counterparty trader, so there is a suspicion of profit transfer and continuous attention is required. Therefore, the two parties are determined as the third our trader and the fifth counterparty trader. The middle circle shows that a certain counterparty trader has negative deviations from multiple traders of our bank, indicating that the counterparty trader has a relatively high quoting level, so there is also a suspicion. Therefore, it needs to be determined as the fifth counterparty trader. The right circle shows that our trader has negative deviations from multiple counterparty traders, indicating that the quoting level of our trader still needs to be improved. Therefore, it is also determined as the third our trader.

[0125] S106. Count the occurrence times of the identified high-risk objects and conduct risk assessment based on the occurrence times.

[0126] Specifically, the more times a trader or a counterparty trading institution is screened in the above steps, the greater its risk, and the more necessary it is to track its transactions. Therefore, in the embodiment of the present application, risk assessment is carried out based on the occurrence times.

[0127] An embodiment of the present application provides a trading risk assessment method, which statistically calculates the trading price deviation degree and trading volume within a target time range according to the first time dimension. Then, it verifies whether each trading price deviation degree and trading volume conform to the normal distribution. If it is detected that each trading price deviation degree and trading volume do not conform to the normal distribution, it determines whether each trading price deviation degree belonging to the fat tail part is greater than the threshold. If it is determined that each trading price deviation degree belonging to the fat tail part is not greater than the threshold, it finds out each preliminary selected our trader and preliminary selected counterparty trading institution whose negative deviation amount meets the preliminary selection conditions. After the preliminary selection, based on the trading data within the time range, it determines high-risk objects. Among them, the high-risk objects include the first our trader, the first counterparty trading institution, the first counterparty trader, the second counterparty trader, the third counterparty trader, the fourth counterparty trader, the second our trader, the second counterparty trading institution, the third our trader, and the fifth counterparty trader; the first our trader and the first counterparty trading institution refer to our trader and counterparty trading institution whose deviation indicators meet the corresponding conditions; the first counterparty trader, the second counterparty trader, and the third counterparty trader refer to counterparty traders whose deviation data and trading volume indicators meet the corresponding conditions; the fourth counterparty trader refers to the counterparty trader who continuously conducts negatively deviated transactions; the second our trader and the second counterparty trading institution refer to our trader and counterparty trading institution suspected of reselling to each other; the third our trader and the fifth counterparty trader refer to our trader and counterparty trader with abnormal trading quotes or a large number of negatively deviated transactions. It counts the occurrence times of the determined high-risk objects and conducts risk assessment based on the occurrence times. Thus, it realizes risk assessment from relevant dimensions of price review, making the result more accurate, and analyzing the overall transaction to avoid ignoring single transactions with small deviations.

[0128] Another embodiment of the present application provides a trading risk assessment device, as Figure 10 shown, including:

[0129] A statistical unit 1001, configured to statistically calculate the trading price deviation degree and trading volume within a target time range according to the first time dimension.

[0130] A verification unit 1002, configured to verify whether each trading price deviation degree and trading volume conform to the normal distribution.

[0131] A judgment unit 1003, configured to determine whether each trading price deviation degree belonging to the fat tail part is greater than the threshold when it is detected that each trading price deviation degree and trading volume do not conform to the normal distribution.

[0132] The primary selection unit 1004 is configured to find out each primary selection our trader, primary selection counterparty trader, and primary selection counterparty institution whose negative deviation amount meets the primary selection conditions when it is determined that the deviation degrees of the transaction prices belonging to the fat-tail part are not greater than the threshold value.

[0133] The object determination unit 1005 is configured to determine high-risk objects based on the transaction data within a time range.

[0134] Among them, the high-risk objects include the first our trader, the first counterparty institution, the first counterparty trader, the second counterparty trader, the third counterparty trader, the fourth counterparty trader, the second our trader, the second counterparty institution, the third our trader, and the fifth counterparty trader. The first our trader and the first counterparty institution refer to our trader and counterparty institution whose deviation indicators meet the corresponding conditions. The first counterparty trader, the second counterparty trader, and the third counterparty trader refer to counterparty traders whose deviation data and trading volume indicators meet the corresponding conditions. The fourth counterparty trader refers to the counterparty trader who continuously conducts negatively deviated transactions. The second our trader and the second counterparty institution refer to our trader and counterparty institution suspected of reselling to each other. The third our trader and the fifth counterparty trader refer to our trader and counterparty trader whose trading quotes are abnormal or have a large number of negatively deviated transactions.

[0135] The risk assessment unit 1006 is configured to count the occurrence times of the determined high-risk objects and perform risk assessment based on the occurrence times.

[0136] It should be noted that for the specific working processes of the various units provided in the above embodiments of the present application, reference can be made to the corresponding specific implementation processes of the corresponding steps in the above method embodiments, which will not be elaborated here.

[0137] Another embodiment of the present application provides an electronic device, as Figure 11 shown, including:

[0138] A memory 1101 and a processor 1102.

[0139] Among them, the memory 1101 is used to store programs;

[0140] The processor 1102 is configured to execute the programs stored in the memory 1101, and when the programs are executed, it is specifically configured to implement the transaction risk assessment method described in any one of the above.

[0141] A fourth aspect of the present application provides a computer storage medium for storing a computer program, which is used to implement the transaction risk assessment method described in any one of the above when the computer program is executed.

[0142] A computer storage medium includes permanent and non-permanent, removable and non-removable media and can implement information storage by any method or technology. The information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassette tapes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transitory medium that can be used to store information that can be accessed by a computing device. As defined herein, computer-readable media does not include transitory computer-readable media such as modulated data signals and carrier waves.

[0143] Those skilled in the art can further realize that the units and algorithm steps of each example described in combination with the embodiments disclosed herein can be implemented by electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described according to functions in the above description. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of this application.

[0144] The above description of the disclosed embodiments enables those skilled in the art to implement or use this application. Various modifications to these embodiments will be obvious to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of this application. Therefore, this application will not be limited to these embodiments shown herein, but rather will conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A trading risk assessment method, characterized in that, it includes: Count the trading price deviation and trading volume within the target time range according to the first time dimension; Verify whether each of the trading price deviations and the trading volume conform to the normal distribution; If it is detected that each of the trading price deviations and the trading volume does not conform to the normal distribution, then determine whether each of the trading price deviations belonging to the fat tail part is greater than the threshold; If it is determined that each of the trading price deviations belonging to the fat tail part is not greater than the threshold, then find out each preliminary candidate our trader and preliminary candidate counterparty trading institution whose negative deviation amount meets the preliminary selection conditions; Based on the trading data within the time range, determine high-risk objects; wherein, the high-risk objects include the first our trader, the first counterparty trading institution, the first counterparty trader, the second counterparty trader, the third counterparty trader, the fourth counterparty trader, the second our trader, the second counterparty trading institution, the third our trader, and the fifth counterparty trader; the first our trader and the first counterparty trading institution refer to our trader and counterparty trading institution whose deviation indicators meet the corresponding conditions; the first counterparty trader, the second counterparty trader, and the third counterparty trader refer to counterparty traders whose deviation data and trading volume indicators meet the corresponding conditions; the fourth counterparty trader refers to the counterparty trader who continuously conducts negative deviation transactions; the second our trader and the second counterparty trading institution refer to our trader and counterparty trading institution that are suspected of reselling each other; the third our trader and the fifth counterparty trader refer to our trader and counterparty trader whose trading quotes are abnormal or have a large number of negative deviation transactions; Count the occurrence times of the determined high-risk objects, and conduct risk assessment based on the occurrence times.

2. The method according to claim 1, characterized in that, the high-risk object is the first our trader, and the determining of the high-risk object based on the trading data within the time range includes: Based on the trading data within the time range, count the total negative deviation and the ratio of the total negative deviation to the personal total trading volume of each of our traders within the target time range according to the second time dimension; Select from each of our traders the our traders whose total negative deviation ranks among the top N and the ratio of the total negative deviation to the personal total trading volume ranks among the top M, and determine them as the first our trader.

3. The method according to claim 1, characterized in that, the high-risk object is the first counterparty trading institution, and the determining of the high-risk object based on the trading data within the time range includes: Based on the trading data within the time range, count the positive deviation amount and negative deviation amount of each counterparty trading institution within the target time range according to the second time dimension; Add the positive deviation amount and negative deviation amount of each counterparty trading institution respectively to obtain the deviation summary value of each counterparty trading institution; Among the respective counterparty trading institutions whose absolute values of the deviation summary values rank among the top L, the counterparty trading institution with the smallest deviation summary value is determined as the first counterparty trading institution.

4. The method according to claim 1, wherein, the high-risk objects are the first counterparty trader, the second counterparty trader, and the third counterparty trader, and determining the high-risk objects based on the trading data within the time range includes: Based on the trading data within the time range, statistically count, according to the second time dimension, the total trading volume, the number of negative deviation transactions, the deviation summary value, and the ratio of the negative amount to the personal trading volume between each of our trading traders and each of the counterparty traders within the target time range; For each of our trading traders, the counterparty traders whose deviation summary values and the ratios of the negative amount to the personal trading volume both rank among the top J among the counterparty traders with whom the trading trader has transactions are determined as the first counterparty traders; Among the respective counterparty traders whose trading volumes with our trading traders rank among the top K, the counterparty trader with the largest ratio of the negative amount to the total personal trading volume is determined as the second counterparty trader; Screen out the counterparty traders whose transactions with our trading traders all have negative deviations, and among the screened counterparty traders, the counterparty trader with the largest deviation summary value among the counterparty traders whose number of negative deviation transactions ranks among the top Q is determined as the third counterparty trader.

5. The method according to claim 1, wherein, the high-risk object is the fourth counterparty trader, and determining the high-risk object based on the trading data within the time range includes: Based on the trading data within the time range, statistically count, according to the second time dimension, the deviation amount of each transaction between each of our trading traders and each of the counterparty traders within the target time range; The counterparty trader whose deviation amount of more than W consecutive transactions with any one of our trading traders is negative is determined as the fourth counterparty trader.

6. The method according to claim 1, wherein, the high-risk objects are the second of our trading traders and the second counterparty trading institution, and determining the high-risk objects based on the trading data within the time range includes: Based on the trading data within the time range, statistically count, according to the second time dimension, the trading volume of the first target transaction within the target time range; wherein, the first target transaction refers to a transaction of the same trading product with the same trading volume at the same price on the same day; Based on the trading volume of the first target transaction, the self-institution conducting the first target transaction, and the counterparty trading institution, draw a first trading map; wherein, the larger the trading volume of the first target transaction, the thicker the connection line between the nodes corresponding to the self-institution and the counterparty trading institution of the first target transaction. Select each of the our traders and counterparty trading institutions that meet the first preset rule based on the first transaction graph as the second our traders and the second counterparty trading institutions.

7. The method according to claim 1, wherein, the high-risk objects are the third our traders and the fifth counterparty traders, and determining the high-risk objects based on the transaction data within the time range includes: Based on the transaction data within the time range, count the second target transactions of the same tradable product and the same counterparty trader in the second time dimension, and the negative deviation amount is greater than the positive deviation amount; Based on the negative deviation amount of the second target transaction, the our trader and the counterparty trader who conduct the second target transaction, draw a second transaction graph; Based on the second transaction graph, determine the our traders whose negative deviation transaction quantity with the same counterparty trader is greater than the preset quantity requirement, and the our traders whose quotation level meets the preset low quotation standard as the third our traders; Determine the counterparty traders whose negative deviation transaction quantity with the same our trader is greater than the preset quantity requirement, and the counterparty traders whose quotation level meets the preset high quotation standard as the fifth counterparty traders.

8. A trading risk assessment device, wherein, comprising: a statistical unit for counting the trading price deviation degree and trading volume within the target time range according to the first time dimension; a verification unit for verifying whether each of the trading price deviation degrees and the trading volume conform to a normal distribution; a judgment unit for judging whether each of the trading price deviation degrees belonging to the fat tail part is greater than a threshold value when it is detected that each of the trading price deviation degrees and the trading volume do not conform to a normal distribution; a primary selection unit for finding out each primary our trader, primary counterparty trader and primary counterparty trading institution whose negative deviation amount meets the primary selection condition when it is judged that each of the trading price deviation degrees belonging to the fat tail part is not greater than the threshold value; An object determination unit, configured to determine high-risk objects based on transaction data within the time range; wherein, the high-risk objects include a first trader on our side, a first counterparty trading institution, a first counterparty trader, a second counterparty trader, a third counterparty trader, a fourth counterparty trader, a second trader on our side, a second counterparty trading institution, a third trader on our side, and a fifth counterparty trader; the first trader on our side and the first counterparty trading institution refer to the trader on our side and the counterparty trading institution whose deviation indicators meet corresponding conditions; the first counterparty trader, the second counterparty trader, and the third counterparty trader refer to the counterparty traders whose deviation data and trading volume indicators meet corresponding conditions; the fourth counterparty trader refers to the counterparty trader who continuously conducts negatively deviated transactions; the second trader on our side and the second counterparty trading institution refer to the trader on our side and the counterparty trading institution that are suspected of reselling to each other; the third trader on our side and the fifth counterparty trader refer to the trader on our side and the counterparty trader whose trading quotes are abnormal or have a large number of negatively deviated transactions. A risk assessment unit, configured to count the occurrence times of the determined high-risk objects and perform risk assessment based on the occurrence times.

9. An electronic device characterized in that it includes: a memory and a processor; wherein, the memory is used to store a program; the processor is used to execute the program, and when the program is executed, it is specifically used to implement the trading risk assessment method according to any one of claims 1 to 7.

10. A computer storage medium characterized in that it is used to store a computer program, and when the computer program is executed, it is used to implement the trading risk assessment method according to any one of claims 1 to 7.

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